To this end, recent works utilize multiple RGB frames acquired from different viewpoints to enhance their pose estimation results [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. In particular, these approaches can be further categorized into offline batch-based solutions [1]}, [3]}, where all the frames are provided at once, and incremental solutions [2]}, [4]}, [5]}, [6]}, where frames are provided sequentially. While fusing pose estimates from different viewpoints can improve the overall performance, handling extreme inconsistency, such as appearance ambiguities, rotational symmetries, and possible occlusions, is still challenging. To address these challenges, in this work, we propose a decoupled formulation to factorize the 6D pose estimation problem into a sequential two-step optimization process. Figure REF  shows an overview of the framework. Based on the per-frame predictions of the object's segmentation mask and 2D center from neural networks, we first optimize the 3D translation and obtain the object's scale in the image. The acquired scale greatly simplifies the object rotation estimation problem with a template-matching method [13]}. A max-mixture formulation [14]} is finally adopted to accommodate the multi-modal output distribution present in rotation space. We conduct extensive experiments on the challenging ROBI dataset [15]}. In comparison to the state-of-the-art method CosyPose [1]}, we achieve a substantial improvement with our method (\(28.5\%\)  and \(3.4\%\)  over its RGB and RGBD version, respectively).
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